Download Efficient Approach for Extracting Frequent Pattern and Association

International Journal of Computer Science Engineering
and Information Technology Research (IJCSEITR)
ISSN 2249-6831
Vol. 3, Issue 3, Aug 2013, 65-78
© TJPRC Pvt. Ltd.
EFFICIENT APPROACH FOR EXTRACTING FREQUENT PATTERN AND
ASSOCIATION RULES WITH PERIODIC CONSTRAINTS
VARSHA MASHORIA1 & ANJU SINGH2
1
Department of Computer Science, Barkatullah University, Institute of Technology, Madhya Pradesh, India
2
Department of Information Technology, Barkatullah University, Institute of Technology, Madhya Pradesh, India
ABSTRACT
Mining of association rule from huge amounts of data in the transaction database plays a vital role in data mining,
but as technology develops it provide us with many techniques for mining rules. For improving the performance and
capability of the association rule mining task, we use different techniques .Constraint based mining is one of them, with the
help of this technique the user can think about closely on mining only interested association rule instead of complete set of
association rule.. In this paper we propose an approach basically for mining association rules with periodic constraint,
which consist of three phases .In first phase it generate the frequent itemset then in the second phase we exploit properties
of periodic constraint and in the last phase it generate all the patterns or rules.
KEYWORDS: Association Rule Mining, Conditional Database, Constraint Based Mining, Frequent Itemsets, Minimum
Support, Periodic Constraints
INTRODUCTION
Data mining is the analysis step of the knowledge Discovery from data (KDD) process. Mining association rules is
a very substantial problem in the data mining field [4]. Which is defined as the non trival eradication of earlier unknown
and potentially useful facts from data in database[27].Discovering association rules between items in a huge transactional
database plays an vital role in data mining research areas. An association rule is an suggestion of the form A
and B are frequent item sets in a transaction database and A∩B = Ǿ. The rule A
B, where A
B can be explained as “if item set A
occurs in a transaction, then item set B will also likely occur in the same transaction”. By such type of facts, market
personnel can place item sets A and B within close immediacy, which may encourage the sale of these items together and
develop discount strategies based on such association/correlation found in the data. Therefore, association rule mining has
received a lot of attention. With the growth of data mining techniques, quite a few researchers have worked on alternative
patterns. In many (but not all) situations, we only care about association rules or idea involving sets of items that appear
frequently in baskets. For example, any one cannot run a good marketing strategy involving items that no one buys anyway.
Thus, much data mining starts with the assumption that we only care about sets of items with high support; i.e., they appear
together in many baskets. We then find association rules or generator only involving a high-support set of items must
appear in at least a certain percent of the baskets, called the support threshold [25].
Frequent pattern mining generate huge amount of frequent itemsets and rules . Frequent patterns are itemsets,
consecutiveness, or substructures that appear in a data set with frequency no less than a user-specified threshold. For
example, a set of items, such as milk and bread that appear frequently together in a transaction data set is a frequent itemset.
A concatenation, such as buying first a PC, then a digital camera, and then a memory card, if it occurs frequently in a
shopping history database, is a (frequent) sequential pattern. A substructure can refer to different structural forms, such as
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sub graphs, sub trees, or sub lattices, which may be combined with itemsets or consecutiveness. If a substructure occurs
frequently in a graph database, it is called a (frequent) structural pattern. Finding frequent patterns plays an essential role in
mining associations, correlations, and many other affecting relationships among data. Moreover, it helps in data indexing,
classification, clustering, and other data mining tasks as well. Thus, frequent pattern mining has become an important data
mining task and a focused theme in data mining research [24]. Frequent pattern mining was first proposed by [3]
Many constraint-based frequent itemset discovery techniques have been proposed for various constraint models
which not only reduce the effectiveness but also reduce the efficiency of mining and the pattern which is generated from the
techniques are repetitious in nature..It plays an essential role in mining associations. Item constraints in frequent itemset
mining were first discussed in [22], which considered the problem of integrating constraints that are Boolean expressions
over the presence or absence of items into the association rule discovery algorithms. [23] Introduced two affecting classes
of itemset constraints: anti-monotonicity and succinctness, and proposed a mining algorithm for handling constraints
belonging to these classes within the Apriori framework, called CAP. [26] Developed a new constraint-based frequent
itemset discovery method, called CFG, which pushed a constraint into the FPgrowth method. In [21], the third class of
constraint, monotonicity, was introduced in the context of mining correlated sets. In [20], more classes of constraints were
introduced: convertible constraints, and methods which enable these classes of constraints to be pushed deep inside the FPgrowth algorithm for frequent itemset mining, were developed. [18] Proposed the Dual Miner algorithm and used both
monotone and anti-monotone constraints to prune the search space. [19] Presented a method to mine itemsets with
restrictions on their variance. [15] Introduced a new class of block constraints that determined the acceptation of an itemset
pattern
In most of the previous constraint based association rule methods the goal is to discover all the patterns whose
frequency in the dataset exceeds a user-specified threshold. However, very often users want to restrict the set of patterns to
be detected by adding extra constraints on the structure of patterns. Data mining systems should be able to exploit such
constraints to speed up the mining process. Techniques applicable to constraint-driven pattern discovery can be classified
into the following groups: ' post-processing (filtering out patterns that do not satisfy user specified pattern constraints after
the actual discovery process); ' pattern filtering (unification of pattern constraints into the actual mining process in order to
generate only patterns satisfying the constraints); ' dataset filtering (restricting the source dataset to objects that can possibly
contain patterns that satisfy pattern constraints)[12]. Wojciechowski and Zakrzewicz [17] focus on improving the efficiency
of constraint- based frequent pattern mining by using dataset filtering techniques. Dataset filtering conceptually transforms
a given data mining task into an equivalent one operating on a smaller dataset. Tien Dung Do et al [13] proposed a specific
type of constraints called category-based as well as the associated algorithm for constrained rule mining based on Apriori.
The Category-based Apriori algorithm reduces the computational complexity of the mining process by get around most of
the subsets of the final itemsets. An experiment has been conducted to show the efficiency of the proposed technique.
In this proposed algorithms we basically used periodic functions as constraints in rule mining techniques. The
periodic constraints functions have infinite number of solution, but usually there are constrained periodic value varied as
min, max interval for the validation of minimum support and confidence parameter for rule generation. The process of rule
mining based on periodic function first abstract the variable, and then eliminates all solution that fall outside at axiom
constraints. The remaining of this paper is organized as follows. In Section 2, we discuss the problem statement. In Section
3, we describe Periodic constraint. Next, In section 4 we describe the related work and work then we describe the main idea
in Section 5, in which we describe Future work. Section 6 consists of a series of experiment or performance evaluation.
Efficient Approach for Extracting Frequent Pattern and Association Rules with Periodic Constraints
67
Conclusions are presented in Section 7.Section 8 represent the future work.
PROBLEM STATEMENT
In the following, we give a formal statement of the problem for which we develop an algorithm for mining all
sophisticated association rules with the help of periodic constraints, so that only sophisticated rule can be generated which
fulfill all the given parameters. The algorithm parameter specifying the minimum confidence bound is known as minconf
and the minimum support bound minsup. A huge number of Association rules can be found from a transactional dataset. To
find the interesting Association rules in a transactional dataset, we must define a specified minimum support (called
minsup) and specified minimum confidence (called minconf). The itemset Y
I is called frequent itemset if sup (X) =>
minsup. It is known that a subset of frequent itemset is a frequent itemset any superset of infrequent itemset is not a frequent
itemset. Finally, the Association rule X→Y holds if conf (X→Y) >= minconf. The Association rules are said to be strong if
it meets the minimum confidence threshold. However, while Association rules provide means to discover many interesting
Associations. Example show how to calculate Association rule from the transaction dataset is explained later. Association
rules are widely used in various areas such as telecommunication networks, market and risk management, inventory control
etc. Various Association mining techniques and algorithms will be briefly introduced and compared later.
In many cases, the algorithms generate an extremely large number of Association rules, often in thousands or even
millions. Further, the Association rules are sometimes very large. It is nearly impossible for the end users to comprehend or
validate such large number of complex Association rules, thereby limiting the usefulness of the Data Mining results.
Several strategies have been proposed to reduce the number of Association rules, such as generating only “interesting”
rules, generating only “non redundant” rules, or generating only those rules satisfying certain other criteria such as
coverage, leverage, lift or strength. In general, Association rule mining can be viewed as a two-step process:
Step 1. Find all frequent itemsets: By definition, each of these itemsets will occur at least as frequently as a predetermined
minimum support count, min sup.
Step 2. Generate strong Association rules from the frequent itemsets: By definition, these rules must satisfy minimum
support and minimum confidence. There are many algorithms is developed for finding frequent patterns and on the
basis of frequent patterns, [29] Association rule is generated.
With the help of constraint we solve this type of problem. Constraints do two things: 1) They limit where the
algorithm can look; and 2) they give hints about where to look.
PERIODIC CONSTRAINTS
In its most general development of entity, the task of data mining is vastly undefined. To make the task more
absolute, we first have to specify the type of patterns considered such as frequent patterns, a clustering, a predictive model
or other invariability in the data. But the detected patterns may not be novel or actionable in fields where domain expertise
already exists or users have strong anticipation.
We then have to specify what conditions the patterns have to satisfy in order to consider them as solutions to the
data mining task at hand. The conditions that a pattern has to satisfy can be elegantly specified as constraints, stated
explicitly and under direct control of the user/data miner. Constraints play vital role in data mining as the use of constraints
enables more efficient data mining and focuses the search for patterns on patterns likely to be of interest to the end user.
The ability to express and exploit constraints allows the data miner to inject knowledge into the process of data mining and
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knowledge discovery.
The different type of constraints is defined for sets [23] that can also be applied in structured data mining. The
structured of data can be ignored and the constraints can be applied as a post processing step of the mining algorithm. An
instance of the periodic constraints is a finite set of “generating” constraints over a large, structured variable set, for
instance, the set of lattice points in k-dimensional space. A correspondent full set of constraints is obtained by repeating the
generating set periodically, and the problem is to decide whether or not the full set of constraints has a satisfying task. This
model is natural for studying large, possibly infinite constraint networks consisting of constraints obeying a high degree of
regularity or symmetry. Such constraint networks arise naturally in many domains, such as scheduling, planning, and
hardware design. So that our results may enjoy maximal applicability.
Definition
A function f(x) is said to be periodic (or when emphasizing the presence of a single periodic [figure 1(a)] instead
of multiple periods singly periodic) with period p if f(x) = f(x + np) for n = 1, 2, 3……as. for e.g. the sin function sinx
illustrated below is periodic with least period (often simple the period) 2Π (as well as will period = 2Π, 4Π, 6Π…..etc).The
constraint function f(x) = 0 is periodic with any periodic with any period R for all non zero real number R1 so there is no
concepts analogeous to the least period for constant function the following definition summarizes the names given to
periodic function based on the number of independent periods they posses.
Let f be a function for item (I1,I2,..........In) there axiom interval (min , max), We define the periodic function in two
mode even and odd.
Even periodic [figure1 (b)]
f= (0<min and min<max)
f (f1,f2,.........,fn) , n<0 and -min<n
Odd periodic [figure1 (c)]
f= (-<max and max<min)
f (f1,f2,.........,fn) , n<0 and -max<n
Figure 1(a): Figure Showing the Basic Concept of Periodic Constraint
Single Periodic Constraints
A function processing a single period in the complex plane is said to be single periodic often simply periodic. This
function includes the trigonometric function.
Efficient Approach for Extracting Frequent Pattern and Association Rules with Periodic Constraints
69
Doubly Periodic Function
A function f(x) is said to be doubly periodic, if it has 2 period W1 and W2 whose ratio W2/W1 is not real. A
doubly periodic function that is analytic (except at poles) and that has no singularities other than poles the finite plane is
called an ellipse function. The period W1 and W2 plays the same part in the theory of elliptic function as does the single
period the case of the trigonometric function.
Figure 1(b): Even Function as Even Constraint
Figure 1(c): Odd Function as Odd Constraint
Triply Periodic Function
A function having 3 distinct periods is known as triply period function. Jacobi 1835 proved that a single – valued
univariate function cannot have more than 2 distinct periods. Thus showing that elliptic function is the most general
multiply periodic single – valued function possible as a single variable.
Analytically, f is an even function if its domain contains the point –x whenever it contains x, and if f (-x) = f (x) for
each x in the domain of f. The function f is an odd function if its domain contains the point –x whenever it contains x, and
if f (-x) = - f (x) for each x in the domain of f.
RELATED WORK
Constrained data mining has been said to be the “best division of labour,” where the computer does the number
crunching and the human provides the focus of attention and direction of the search by providing search constraints.[9]
Constraints do two things: 1) They limit where the algorithm can look; and 2) they give hints about where to look. [5] As a
constraint is a guide to direct the search, combining knowledge with inductive logic programming is a type of constraint,
and that knowledge directs the search and limits the results. This combination is extremely effective [16]. If every possible
pattern is selected and the constraints tested afterwards, then the search space becomes large and the time required to
perform this becomes excessive. [2] The constraints must be in place during the search. They can be as simple as thresholds
on rule quality measure support or confidence, or more complicated logic to formulate various conditions. [11] In mining
with a structured query language (SQL), the constraint can be a predicate for association rules. [17] In this case, the rule has
a constraint limiting which records to select. This can either be the total job or produce data for a next stage of refinement.
For example, in a large database of bank transactions, one could specify only records of ACH transactions that occurred
during the first half of this year. This reduces the search space for the next process. A typical search would be: select *
where year = 2006 and where month < 7 It might be necessary that two certain types always cluster together (must-link), or
the opposite, that they may never be in the same cluster (cannot-link).[6] In clustering (except fuzzy clustering), elements
either are or are not in the same cluster. [2] Application of this to the above example could further require that the
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transactions must have occurred on the first business day of the week (must-link), even further attenuating the dataset. It
could be even further restricted by adding a cannot-link rule such as not including a national holiday. In the U.S.A., this
rule would reduce the search space by a little over 10 percent. The rule would be similar to: select * where day = Monday
and day <8 and where day \=holiday If mining with a decision tree, pruning is an effective way of applying constraints. This
has the effect of pruning the clustering dendogram (clustering tree). If none of the elements on the branch meet the
constraints, then the entire branch can be pruned [2] In Ravi and Davidson's study of image location for a robot, the savings
from pruning were between 50 percent and 80 percent. There was also a typical improvement of a 15 percent reduction in
distortion in the clusters, and the class label purity improved. Applying this to the banking example, any branch that had a
Monday national holiday could be deleted. This would save about five weeks a year, or about 10 percent.
Mining a large data set can be time consuming, and without constraints, the process could generate sets or rules
that are invalid or redundant. Some methods, for example association rule mining, are effective, but can be extremely time
consuming for large data sets. As the set grows in size, the processing time grows exponentially. In other situations, without
guidance via constraints, the data mining process might find morsels that have no relevance to the topic or are trivial and
hence worthless However, frequent itemset mining algorithms often generate a large number of frequent itemsets and rules,
which reduce not only the efficiency but also the effectiveness of the mining algorithms since only the subset of the
complete frequent itemsets and association rules is of interest to users, and users need additional post-processing to filter
through a large number of mined rules to find the useful ones. Constraint-based mining enables users to provide restraints to
search for those useful ones. It concentration itemsets that are interesting from a user’s point of view, rather than wasting
time on discovering itemsets the user has not asked for. Many constraint-based frequent itemset discovery techniques have
been proposed recently for various constraint models. Item constraints in frequent itemset mining were first discussed in
[24], which considered the problem of integrating constraints that are Boolean expressions over the presence or absence of
items into the association rule discovery algorithms.[17] introduced two interesting classes of itemset constraints: antimonotonicity and succinctness, and proposed a mining algorithm for handling constraints belonging to these classes within
the Apriori Framework, called CAP.[18] developed a new constraint-based frequent itemset discovery method, called CFG,
which pushed a constraint into the FPgrowth method. In [8], the third class of constraint, monotonicity, was introduced in
the context of mining correlated sets. In [19], more classes of constraints were introduced: convertible constraints, and
methods which enable these classes of constraints to be pushed deep inside the FP-growth algorithm for frequent itemset
mining, were developed.[4] proposed the DualMiner algorithm and used both monotone and anti-monotone constraints to
prune the search space.[14] presented a method to mine itemsets with restrictions on their variance.[7] introduced a new
class of block constraints that determined the significance of an itemset pattern. In most of previous constraint-based
frequent itemset mining methods, an item in a transaction is identified by its item ID and characterized only by a single
attribute value (such as price). In some circumstances, users may want to keep records of items with more than one
attribute.
So we in this paper present a method of association rule mining with periodic constraint, with the help of which we
mine the association rule which is of user interest. So that all the redundant and invalid rules which does not satisfy the user
specified condition. In comparison with the Apriori algorithm [1], the FP-growth algorithm [10], is more efficient to mine
frequent itemsets. Furthermore, FP-growth-based methods also achieve performance gain in constraint-based mining by the
employment of conditional databases. More properties of constraints can be pushed into FP-growth-based methods than
Apriori-like methods. Therefore, in this paper, we choose the FP-growth method as the basic approach in our model and
develop the algorithms to mine frequent itemsets.
Efficient Approach for Extracting Frequent Pattern and Association Rules with Periodic Constraints
71
PROPOSED WORK AND PROPOSED MINING ALGORITHM
Association Rule Mining is key player of pattern analysis of affecting data. The size of data is big and the
generation of pattern and rule is huge. The generated huge pattern and rule are no meaningful. So now in these day
constraints based association rule mining algorithm is applied. The constraints factor reduces the unnecessary rule set in
process of rule generation. Some constraints based method such as monotonic and non monotonic or anti monotonic. These
are very effective for certain valid pattern generation but lacking some constraints factor such as continuity factor generates
some invalid rule.
For remedies of these processes we used periodic functions as constraints in rule mining techniques. The periodic
constraints functions have infinite number of solution, but usually there are constrained periodic value varied as min, max
interval for the validation of minimum support and confidence parameter for rule generation. The process of rule mining
based on periodic function first abstract the variable, and then eliminates all solution that fall outside at axiom constraints.
Figure 2: Data Flow Diagram
Now in mining algorithm by fulfilling all condition put by periodic constraint all frequent items are generated. In
this work the proposed algorithm deal with 2 periodic constraints that is odd constraints and even constraints. A periodic
constraints C is convertible into odd constraints and even constraints. The set of constraints are not statically the rule
mining process the set of rule generation is Ø. After generating frequent item we apply FP-Growth algorithm so that all
rules can be generated by it. The dataflow diagram in Figure 2 is a solution to this problem. There is one decision symbol in
the data flow diagram, which checks for constraint. If the frequent item generated does not fulfill the constraints condition it
will go back to second step and if the frequent item generated fulfill the condition of constraint it will proceeded to next
step. In next step the item generated by the decision symbol is divided into odd and even. after this we find the set of odd
and even items the condition is check for Ø. After this we use the FP-Growth algorithm to generate the rules.
Scan each item from the transaction table.
Priori compute even and odd set of items according to given minimum and maximum condition.
Mapping statically all constraint itemset and their support.
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Eliminate the value of outside constraints.
Sort the even and odd itensets.
Check for (i=1; Eoij≠0;i++)
Eoij = FPgrowth(Eoij) generate new coordinate.
Delete all itemsets generate from the outage variable in set Eoij.
return frequent rules.
RESULTS AND DISCUSSIONS
For evaluation purpose, we have conducted several experiments. We select the well-known IBM synthetic
database generated by the generator in [28]. This choice gives us the flexibility of controlling the size and dimension of the
database. All of experiments are performed on Pentium IV processor with a 3.4 GHz processor and 2 GB memory. The
program is developed in MATLAB 7.8.0. In this paper, the runtime includes both CPU time and I/O time. In all of the
following experiments, we limit the length of association rules. Since our algorithms can be applied to mine association
rules among frequent items with bounded length, we compare our schemes with the most influential algorithms for
association rule mining, which are the Apriori algorithm and FP-Tree. . The parameter we used for compare our algorithm
are maximum (max) value, minimum (min) value, minimum support (minsup) and minimum confidence (minconf). The
calculated result are in the form of number of rule generated and the execution time it takes to generate these rules with the
given parameter. The values of parameter are changed every time and the values are recorded in different tables this figure
shows the comparative result between Apriori, FP-Tree and our proposed algorithm. All the values generated from this can
be plotted on graph.
Table 1(a): Shows the Calculated Result of Generated Rules
with Apriori, FP-Tree and Our Proposed Approach
Figure 3(a): Shows the Comparission Graph between Number of Rules Generated by
Using Different Dataset Chess, Wine, Mushroom and with Different Methods
Efficient Approach for Extracting Frequent Pattern and Association Rules with Periodic Constraints
Table 1(b): Shows the Calculated Result of Execution Time
Taken by these Approach to Generate the Rules
Figure 3(b): Shows the Comparission Graph between Execution Time Taken by
Different Dataset (Chess, Wine, Mushroom.etc) and with Different Methods
Table 2(a): Shows the Calculated Result of Generated Rules
Apriori, FP-Tree and Our Proposed Approach
Figure 4(a): Shows the Comparission Graph between Number with of Rules Generated by
Using Different Dataset (Chess, Wine, Mushroom etc) and with Different Methods
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Table 2(b): Shows the Calculated Result of Execution Time
Taken by these Approach to Generate the Rules
Figure 4(b): Shows the Comparission Graph between Execution Time Taken by
Different Dataset (Chess, Wine, Mushroom.etc) and with Different Methods
Table 3(a): Shows the Calculated Result of Generated Rules
with Apriori, FP-Tree and our Proposed Approach
Figure 5(a): Shows the Comparission Graph between Number of Rules Generated by
Using Different Dataset Chess, Wine, Mushroom etc) and with Different Methods
Efficient Approach for Extracting Frequent Pattern and Association Rules with Periodic Constraints
75
Table 3(b): Shows the Calculated Result of Execution Time
Taken by these Approach to Generate the Rules
Figure 5(b): Shows the Compression Graph between Execution Time Taken by
Different Dataset (Chess, Wine, Mushroom.etc) and with Different Methods
CONCLUSIONS
In this paper we proposed a novel method for pattern mining based on periodic constraints. As we know that
constraints are the condition that a pattern has to satisfied. With the help of periodic constraint we generate all valid (user
defined) rules and all the unnecessary rules which does not satisfy user level are reduced or deleted. In this paper we use
periodic functions as constraints in rule mining techniques, Usually there are constrained periodic value varied as min, max
interval for the validation of minimum support and confidence parameter for rule generation. The process of rule mining
based on periodic function first abstract the variable, and then eliminates all solution that fall outside at axiom constraints.
The set of constraint are not statically, the rule mining process the set of rule generation is Ø. Our experimental result
shows that our algorithm attains very good mining efficiencies on various input datasets.
FUTURE WORK
The limitation of this algorithm is that the execution time increase as our dataset increases. So these investigations
have identified some interesting directions for future research:

In the case of minimum, maximum condition occurred a data over fitting the situation of rules generation is all
positive and all negative.

We also hide the sensitive pattern using minimum, maximum condition ( Data preserving ).
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